In the field of imaging, spectral imaging provides a way to qualify the spatial and spectral characteristics of a scene within a field of view. Spectral imaging may be used to detect such as point threats that are dynamic in nature, and which occur and evolve rapidly in time with respect to their environment. An example of this kind of detection could be the observation of an explosion from a great distance as from an airborne platform or space. Various devices are used to provide spectral imaging of an event from a scene on a single detector or an array of detectors. Imaging spectrometers can be scanning devices or spatially multiplexing imagers that scan a slit over the entire field of view of the sensor to acquire the complete image. In this case, the sensor is a modification of a prism or a grating spectrometer requiring a two dimensional detector at its output. The output of the scanned slit instrument is a two dimensional image from the detector where one dimension is spectral information and the second dimension is spatial information. A full image is created by sweeping the scene under view with the scanning slit to obtain the second spatial dimension on a line by line basis. The data product from this spectral imager is a three dimensional cube possessing two spatial dimensions and one spectral dimension. Another option is spectrally filtered imaging. Generally, these devices entail acquiring one spectral band per image until the required number of filtered images “n” is scanned to complete the covered spectrum. FIG. 1 shows a spectral data cube produced from such a system where there are “n” number of images covering “n” spectral bands. In the case of multispectral systems, there are band pass filters for each spectral band. In addition to generating large quantities of data for analysis, these devices can miss rapidly changing spectral features in any band (or bands) since only one color is collected per frame. Scanned slit systems can miss a rapidly evolving event entirely if the event duration is less than a frame time and/or the scan slit is not covering the event area in its field of view when the event occurs. This situation could occur with gunfire or other rapidly-occurring point events.
Spectral imaging, whether multi-spectral or hyper-spectral, generally involves determining the spectral and spatial characteristics of a scene in the field of view of an imaging system or sensor. An imaging sensor or imaging spectrometer includes an optical system that provides images of its field of view in various spectral bands, so that for “n” bands or “n” channels (the terms “channels” and “bands” are used interchangeably) there will be the same number “n” of two-dimensional images completing a data-cube. These devices have the capability of acquiring a large amount of spectral data with high frame rates over a field of view. In addition to the fact that the large amount of spectral data requires greater processing capacity for analysis, the optical front-ends of many of these spectrometers include complex optics that have optics with mechanical parts and/or may require precision alignment. To retrofit one of these optical front-end devices onto existing munitions and imagers is extremely difficult, cost prohibitive, and in some cases impossible due to the size and weight constraints. Other multi-spectral imaging devices use rotating filters that only record a single image at a time or use a plurality of imaging and detection systems that cannot image an event onto a single detector array. Traditional multi- or hyper-spectral imagers process tens or hundreds of spectral bands that produce responses of varying amplitude and shape relative to a particular threat or a set of threats. Processing all of these bands is challenging and requires greater hardware capacity and time to process the data. It also is more prone to errors due to the output response of various sources that may be present in the field of view and that may amplify or attenuate the signal of the event being detected. This situation results in false alarms or missed targets due to the above mentioned artifacts.
Another option is developing new imagers. However, in addition to the cost and complexity factors of designing such devices, the data processing requirements will add time and expense to the data analysis hardware of the existing munitions and imagers. The “n” channels will produce “n” images and the signal processing algorithms will analyze the spectral contents of each channel to determine if the dynamic point like source is a threat or a benign event.
As a result, a need exists for an imaging device that can i) rapidly and reliably characterize and analyze spectral data in its field of view, particularly spectral data of point events that are small and discrete relative to the field of view scanned by the imager, ii) do so without requiring moving parts, constant precision alignment in the field of view, or maintenance of an out of field view, and iii) be retrofitted onto existing imagers to provide a persistent wide field of view capability for multispectral detection of point events without being cost prohibitive.